Autors: Mihaylova, D. A.
Title: Baseline Adversarial Machine Learning Attacks - A Comparative Study
Keywords: Adversarial Machine Learning, Carlini&Wagner, DeepFool, Fast Gradient Sign Method, Jacobian-based Saliency Map Attack

Abstract: Machine learning is recognized as one of the foremost technologies that are going to make an impact on Next Generation Wireless Systems. Among its numerous applications in different fields, it is acknowledged as a valuable tool in the domain of computer vision, intrusion detection systems, autonomous driving and much more. However, a great challenge to the robust operation of machine learning systems represents their vulnerability to adversarial machine learning (AML) attacks. In this paper, four methods of significant importance for crafting AML examples are studied, namely the Fast Gradient Sign Method, Jacobian-based Saliency Map Attack, DeepFool, Carlini & Wagner attack. The methods are compared according to several properties that can have critical influence on their implementation in various technology domains.

References

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Issue

60th International Scientific Conference on Information, Communication and Energy Systems and Technologies, ICEST 2025 - Proceedings, 2025, Macedonia, https://doi.org/10.1109/ICEST66328.2025.11098269

Цитирания (Citation/s):
1. Lekidis A., Yigit Y., Maglaras L.A., Karantzalos K., Spanoudakis G., Next-Gen Security Operation Center Services for Critical National Infrastructures, 2026, Electronics Switzerland, issue 15, vol. 15, DOI 10.3390/electronics15153248, eissn 20799292 - 2026 - в издания, индексирани в Scopus

Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus